Overview

Dataset statistics

Number of variables14
Number of observations111857
Missing cells2694
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.9 MiB
Average record size in memory112.0 B

Variable types

Categorical5
Numeric9

Alerts

User Comments Added has constant value "0" Constant
Date has a high cardinality: 1523 distinct values High cardinality
Video Title has a high cardinality: 223 distinct values High cardinality
External Video ID has a high cardinality: 223 distinct values High cardinality
Thumbnail link has a high cardinality: 223 distinct values High cardinality
Video Length is highly correlated with Average View PercentageHigh correlation
Views is highly correlated with Video Likes Added and 1 other fieldsHigh correlation
Video Likes Added is highly correlated with Views and 1 other fieldsHigh correlation
User Subscriptions Added is highly correlated with Views and 1 other fieldsHigh correlation
Average View Percentage is highly correlated with Video LengthHigh correlation
Views is highly correlated with Video Likes Added and 3 other fieldsHigh correlation
Video Likes Added is highly correlated with Views and 2 other fieldsHigh correlation
Video Dislikes Added is highly correlated with Views and 1 other fieldsHigh correlation
Video Likes Removed is highly correlated with Video Dislikes AddedHigh correlation
User Subscriptions Added is highly correlated with Views and 1 other fieldsHigh correlation
User Subscriptions Removed is highly correlated with Views and 1 other fieldsHigh correlation
Views is highly correlated with Video Likes AddedHigh correlation
Video Likes Added is highly correlated with Views and 1 other fieldsHigh correlation
User Subscriptions Added is highly correlated with Video Likes AddedHigh correlation
Views is highly correlated with Video Likes Added and 4 other fieldsHigh correlation
Video Likes Added is highly correlated with Views and 4 other fieldsHigh correlation
Video Dislikes Added is highly correlated with Views and 4 other fieldsHigh correlation
Video Likes Removed is highly correlated with Views and 2 other fieldsHigh correlation
User Subscriptions Added is highly correlated with Views and 3 other fieldsHigh correlation
User Subscriptions Removed is highly correlated with Views and 3 other fieldsHigh correlation
Average View Percentage has 1347 (1.2%) missing values Missing
Average Watch Time has 1347 (1.2%) missing values Missing
Views is highly skewed (γ1 = 43.93953763) Skewed
Video Likes Added is highly skewed (γ1 = 36.19381899) Skewed
Video Dislikes Added is highly skewed (γ1 = 139.9320946) Skewed
Video Likes Removed is highly skewed (γ1 = 190.5669346) Skewed
User Subscriptions Added is highly skewed (γ1 = 70.58319419) Skewed
User Subscriptions Removed is highly skewed (γ1 = 31.48082153) Skewed
Views has 1347 (1.2%) zeros Zeros
Video Likes Added has 69081 (61.8%) zeros Zeros
Video Dislikes Added has 109015 (97.5%) zeros Zeros
Video Likes Removed has 106332 (95.1%) zeros Zeros
User Subscriptions Added has 86412 (77.3%) zeros Zeros
User Subscriptions Removed has 110050 (98.4%) zeros Zeros

Reproduction

Analysis started2022-04-04 01:11:15.524517
Analysis finished2022-04-04 01:11:37.139823
Duration21.62 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY

Distinct1523
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size874.0 KiB
4 Oct 2021
 
202
20 Sept 2021
 
201
27 Dec 2021
 
192
29 Dec 2021
 
190
30 Dec 2021
 
190
Other values (1518)
110882 

Length

Max length12
Median length11
Mean length10.79518492
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique294 ?
Unique (%)0.3%

Sample

1st row19 Jan 2022
2nd row19 Jan 2022
3rd row19 Jan 2022
4th row19 Jan 2022
5th row19 Jan 2022

Common Values

ValueCountFrequency (%)
4 Oct 2021202
 
0.2%
20 Sept 2021201
 
0.2%
27 Dec 2021192
 
0.2%
29 Dec 2021190
 
0.2%
30 Dec 2021190
 
0.2%
6 Jan 2022189
 
0.2%
16 Jan 2022189
 
0.2%
19 Dec 2021189
 
0.2%
18 Jan 2022189
 
0.2%
23 Dec 2021188
 
0.2%
Other values (1513)109938
98.3%

Length

2022-04-04T01:11:37.267546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
202161222
18.2%
202039267
 
11.7%
dec11614
 
3.5%
nov10940
 
3.3%
oct10863
 
3.2%
sept10123
 
3.0%
jan10081
 
3.0%
aug10065
 
3.0%
jul9614
 
2.9%
jun8772
 
2.6%
Other values (39)153010
45.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Video Title
Categorical

HIGH CARDINALITY

Distinct223
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size874.0 KiB
Predicting Crypto-Currency Price Using RNN lSTM & GRU
 
1522
How to Simulate NBA Games in Python
 
1105
How I Became A Data Scientist From a Business Background
 
1046
Should You Get A Masters in Data Science?
 
1029
Work From Home Data Scientist: Day in the Life
 
1015
Other values (218)
106140 

Length

Max length100
Median length53
Mean length53.70190511
Min length21

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKaggle Project From Scratch - Part 2 (Exploratory Data Analysis)
2nd rowWelcome To My Channel | Ken Jee | Data Science
3rd rowHow She Dominated the FAANG Data Science Interview (@Tina Huang ) - KNN EP. 11
4th rowThe 9 Books That Changed My Perspective in 2019
5th rowInterview with the Director of AI Research @ NVIDIA (Anima Anandkumar) - KNN EP. 07

Common Values

ValueCountFrequency (%)
Predicting Crypto-Currency Price Using RNN lSTM & GRU1522
 
1.4%
How to Simulate NBA Games in Python1105
 
1.0%
How I Became A Data Scientist From a Business Background1046
 
0.9%
Should You Get A Masters in Data Science?1029
 
0.9%
Work From Home Data Scientist: Day in the Life1015
 
0.9%
Scrape Twitter Data in Python with Twitterscraper Module1005
 
0.9%
Predicting Season Long NBA Wins Using Multiple Linear Regression974
 
0.9%
Should You Learn R for Data Science?973
 
0.9%
My Top 5 Data Science Resources for 2019951
 
0.9%
Where YOU Should Start With Data Science Projects947
 
0.8%
Other values (213)101290
90.6%

Length

2022-04-04T01:11:37.509304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
data100150
 
9.7%
science76061
 
7.4%
38835
 
3.8%
to25369
 
2.5%
a25312
 
2.5%
the24176
 
2.4%
how20324
 
2.0%
your15043
 
1.5%
projects14899
 
1.4%
you14684
 
1.4%
Other values (610)673514
65.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

External Video ID
Categorical

HIGH CARDINALITY

Distinct223
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size874.0 KiB
qfRhKHV8-t4
 
1522
irjTWNV0eAY
 
1105
IFceyuL6GZY
 
1046
RRSRKf9eQxc
 
1029
4CpmB4TR2C4
 
1015
Other values (218)
106140 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKQ80oD_boBM
2nd rowsmeFkHwnM_k
3rd rowvfV4nm004VQ
4th row3TrAYmrmA8o
5th rowXgg7dIKys9E

Common Values

ValueCountFrequency (%)
qfRhKHV8-t41522
 
1.4%
irjTWNV0eAY1105
 
1.0%
IFceyuL6GZY1046
 
0.9%
RRSRKf9eQxc1029
 
0.9%
4CpmB4TR2C41015
 
0.9%
zF_Q2v_9zKY1005
 
0.9%
Y_SMU701qlA974
 
0.9%
AxP1CL0yaFQ973
 
0.9%
tv1e22u2COk951
 
0.9%
sq5TnVJWv6A947
 
0.8%
Other values (213)101290
90.6%

Length

2022-04-04T01:11:37.714715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
qfrhkhv8-t41522
 
1.4%
irjtwnv0eay1105
 
1.0%
ifceyul6gzy1046
 
0.9%
rrsrkf9eqxc1029
 
0.9%
4cpmb4tr2c41015
 
0.9%
zf_q2v_9zky1005
 
0.9%
y_smu701qla974
 
0.9%
axp1cl0yafq973
 
0.9%
tv1e22u2cok951
 
0.9%
sq5tnvjwv6a947
 
0.8%
Other values (213)101290
90.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Video Length
Real number (ℝ≥0)

HIGH CORRELATION

Distinct202
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean874.6158756
Minimum47
Maximum5029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size874.0 KiB
2022-04-04T01:11:37.896600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile216
Q1375
median548
Q3917
95-th percentile2686
Maximum5029
Range4982
Interquartile range (IQR)542

Descriptive statistics

Standard deviation861.2976669
Coefficient of variation (CV)0.9847725052
Kurtosis4.773003066
Mean874.6158756
Median Absolute Deviation (MAD)211
Skewness2.194913609
Sum97831908
Variance741833.671
MonotonicityNot monotonic
2022-04-04T01:11:38.121355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4671933
 
1.7%
3751757
 
1.6%
3111522
 
1.4%
4951484
 
1.3%
4841397
 
1.2%
2911383
 
1.2%
3921333
 
1.2%
591277
 
1.1%
3781214
 
1.1%
5561105
 
1.0%
Other values (192)97452
87.1%
ValueCountFrequency (%)
47224
 
0.2%
51474
 
0.4%
53212
 
0.2%
556
 
< 0.1%
56436
 
0.4%
5741
 
< 0.1%
591277
1.1%
60167
 
0.1%
114169
 
0.2%
128427
 
0.4%
ValueCountFrequency (%)
5029149
 
0.1%
5005153
 
0.1%
4518478
0.4%
4119651
0.6%
4112555
0.5%
373596
 
0.1%
3706428
0.4%
3659367
0.3%
3493398
0.4%
3413168
 
0.2%

Thumbnail link
Categorical

HIGH CARDINALITY

Distinct223
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size874.0 KiB
https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg
 
1522
https://i.ytimg.com/vi/irjTWNV0eAY/hqdefault.jpg
 
1105
https://i.ytimg.com/vi/IFceyuL6GZY/hqdefault.jpg
 
1046
https://i.ytimg.com/vi/RRSRKf9eQxc/hqdefault.jpg
 
1029
https://i.ytimg.com/vi/4CpmB4TR2C4/hqdefault.jpg
 
1015
Other values (218)
106140 

Length

Max length48
Median length48
Mean length48
Min length48

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://i.ytimg.com/vi/KQ80oD_boBM/hqdefault.jpg
2nd rowhttps://i.ytimg.com/vi/smeFkHwnM_k/hqdefault.jpg
3rd rowhttps://i.ytimg.com/vi/vfV4nm004VQ/hqdefault.jpg
4th rowhttps://i.ytimg.com/vi/3TrAYmrmA8o/hqdefault.jpg
5th rowhttps://i.ytimg.com/vi/Xgg7dIKys9E/hqdefault.jpg

Common Values

ValueCountFrequency (%)
https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg1522
 
1.4%
https://i.ytimg.com/vi/irjTWNV0eAY/hqdefault.jpg1105
 
1.0%
https://i.ytimg.com/vi/IFceyuL6GZY/hqdefault.jpg1046
 
0.9%
https://i.ytimg.com/vi/RRSRKf9eQxc/hqdefault.jpg1029
 
0.9%
https://i.ytimg.com/vi/4CpmB4TR2C4/hqdefault.jpg1015
 
0.9%
https://i.ytimg.com/vi/zF_Q2v_9zKY/hqdefault.jpg1005
 
0.9%
https://i.ytimg.com/vi/Y_SMU701qlA/hqdefault.jpg974
 
0.9%
https://i.ytimg.com/vi/AxP1CL0yaFQ/hqdefault.jpg973
 
0.9%
https://i.ytimg.com/vi/tv1e22u2COk/hqdefault.jpg951
 
0.9%
https://i.ytimg.com/vi/sq5TnVJWv6A/hqdefault.jpg947
 
0.8%
Other values (213)101290
90.6%

Length

2022-04-04T01:11:38.504010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://i.ytimg.com/vi/qfrhkhv8-t4/hqdefault.jpg1522
 
1.4%
https://i.ytimg.com/vi/irjtwnv0eay/hqdefault.jpg1105
 
1.0%
https://i.ytimg.com/vi/ifceyul6gzy/hqdefault.jpg1046
 
0.9%
https://i.ytimg.com/vi/rrsrkf9eqxc/hqdefault.jpg1029
 
0.9%
https://i.ytimg.com/vi/4cpmb4tr2c4/hqdefault.jpg1015
 
0.9%
https://i.ytimg.com/vi/zf_q2v_9zky/hqdefault.jpg1005
 
0.9%
https://i.ytimg.com/vi/y_smu701qla/hqdefault.jpg974
 
0.9%
https://i.ytimg.com/vi/axp1cl0yafq/hqdefault.jpg973
 
0.9%
https://i.ytimg.com/vi/tv1e22u2cok/hqdefault.jpg951
 
0.9%
https://i.ytimg.com/vi/sq5tnvjwv6a/hqdefault.jpg947
 
0.8%
Other values (213)101290
90.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Views
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1545
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.71428699
Minimum0
Maximum35677
Zeros1347
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size874.0 KiB
2022-04-04T01:11:38.691093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median9
Q328
95-th percentile151
Maximum35677
Range35677
Interquartile range (IQR)25

Descriptive statistics

Standard deviation316.5575147
Coefficient of variation (CV)6.367536051
Kurtosis3395.060358
Mean49.71428699
Median Absolute Deviation (MAD)7
Skewness43.93953763
Sum5560891
Variance100208.6601
MonotonicityNot monotonic
2022-04-04T01:11:38.911152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113806
 
12.3%
29821
 
8.8%
37404
 
6.6%
45894
 
5.3%
54841
 
4.3%
64086
 
3.7%
73685
 
3.3%
83313
 
3.0%
93016
 
2.7%
102782
 
2.5%
Other values (1535)53209
47.6%
ValueCountFrequency (%)
01347
 
1.2%
113806
12.3%
29821
8.8%
37404
6.6%
45894
5.3%
54841
 
4.3%
64086
 
3.7%
73685
 
3.3%
83313
 
3.0%
93016
 
2.7%
ValueCountFrequency (%)
356771
< 0.1%
314731
< 0.1%
266681
< 0.1%
203411
< 0.1%
161441
< 0.1%
154101
< 0.1%
152351
< 0.1%
145851
< 0.1%
144191
< 0.1%
138751
< 0.1%

Video Likes Added
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct272
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.095532689
Minimum0
Maximum1610
Zeros69081
Zeros (%)61.8%
Negative0
Negative (%)0.0%
Memory size874.0 KiB
2022-04-04T01:11:39.128286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum1610
Range1610
Interquartile range (IQR)1

Descriptive statistics

Standard deviation14.26369466
Coefficient of variation (CV)6.806715418
Kurtosis2511.935989
Mean2.095532689
Median Absolute Deviation (MAD)0
Skewness36.19381899
Sum234400
Variance203.4529854
MonotonicityNot monotonic
2022-04-04T01:11:39.352809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
069081
61.8%
120179
 
18.0%
27917
 
7.1%
34146
 
3.7%
42486
 
2.2%
51642
 
1.5%
61056
 
0.9%
7798
 
0.7%
8514
 
0.5%
9417
 
0.4%
Other values (262)3621
 
3.2%
ValueCountFrequency (%)
069081
61.8%
120179
 
18.0%
27917
 
7.1%
34146
 
3.7%
42486
 
2.2%
51642
 
1.5%
61056
 
0.9%
7798
 
0.7%
8514
 
0.5%
9417
 
0.4%
ValueCountFrequency (%)
16101
< 0.1%
12271
< 0.1%
7741
< 0.1%
7111
< 0.1%
7091
< 0.1%
6571
< 0.1%
6561
< 0.1%
6311
< 0.1%
6261
< 0.1%
6131
< 0.1%

Video Dislikes Added
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05242407717
Minimum0
Maximum289
Zeros109015
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size874.0 KiB
2022-04-04T01:11:39.552291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum289
Range289
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.852012656
Coefficient of variation (CV)35.3275204
Kurtosis20793.11136
Mean0.05242407717
Median Absolute Deviation (MAD)0
Skewness139.9320946
Sum5864
Variance3.429950876
MonotonicityNot monotonic
2022-04-04T01:11:39.739004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0109015
97.5%
12176
 
1.9%
2370
 
0.3%
3119
 
0.1%
469
 
0.1%
534
 
< 0.1%
614
 
< 0.1%
912
 
< 0.1%
710
 
< 0.1%
87
 
< 0.1%
Other values (17)31
 
< 0.1%
ValueCountFrequency (%)
0109015
97.5%
12176
 
1.9%
2370
 
0.3%
3119
 
0.1%
469
 
0.1%
534
 
< 0.1%
614
 
< 0.1%
710
 
< 0.1%
87
 
< 0.1%
912
 
< 0.1%
ValueCountFrequency (%)
2892
< 0.1%
2841
< 0.1%
2691
< 0.1%
2021
< 0.1%
641
< 0.1%
481
< 0.1%
361
< 0.1%
351
< 0.1%
321
< 0.1%
281
< 0.1%

Video Likes Removed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08678938287
Minimum0
Maximum420
Zeros106332
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size874.0 KiB
2022-04-04T01:11:39.928407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum420
Range420
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.793531685
Coefficient of variation (CV)20.66533516
Kurtosis41055.01462
Mean0.08678938287
Median Absolute Deviation (MAD)0
Skewness190.5669346
Sum9708
Variance3.216755906
MonotonicityNot monotonic
2022-04-04T01:11:40.116812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0106332
95.1%
14160
 
3.7%
2678
 
0.6%
3284
 
0.3%
4168
 
0.2%
5107
 
0.1%
639
 
< 0.1%
735
 
< 0.1%
813
 
< 0.1%
910
 
< 0.1%
Other values (19)31
 
< 0.1%
ValueCountFrequency (%)
0106332
95.1%
14160
 
3.7%
2678
 
0.6%
3284
 
0.3%
4168
 
0.2%
5107
 
0.1%
639
 
< 0.1%
735
 
< 0.1%
813
 
< 0.1%
910
 
< 0.1%
ValueCountFrequency (%)
4201
< 0.1%
3551
< 0.1%
1511
< 0.1%
741
< 0.1%
531
< 0.1%
381
< 0.1%
311
< 0.1%
251
< 0.1%
231
< 0.1%
201
< 0.1%

User Subscriptions Added
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct198
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.169180293
Minimum0
Maximum1844
Zeros86412
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size874.0 KiB
2022-04-04T01:11:40.328964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum1844
Range1844
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.10906503
Coefficient of variation (CV)10.35688431
Kurtosis8300.19818
Mean1.169180293
Median Absolute Deviation (MAD)0
Skewness70.58319419
Sum130781
Variance146.629456
MonotonicityNot monotonic
2022-04-04T01:11:40.549644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
086412
77.3%
113416
 
12.0%
24322
 
3.9%
32134
 
1.9%
41258
 
1.1%
5839
 
0.8%
6604
 
0.5%
7435
 
0.4%
8304
 
0.3%
9251
 
0.2%
Other values (188)1882
 
1.7%
ValueCountFrequency (%)
086412
77.3%
113416
 
12.0%
24322
 
3.9%
32134
 
1.9%
41258
 
1.1%
5839
 
0.8%
6604
 
0.5%
7435
 
0.4%
8304
 
0.3%
9251
 
0.2%
ValueCountFrequency (%)
18441
< 0.1%
15901
< 0.1%
8031
< 0.1%
7411
< 0.1%
7181
< 0.1%
6851
< 0.1%
6301
< 0.1%
5871
< 0.1%
5711
< 0.1%
5331
< 0.1%

User Subscriptions Removed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0263908383
Minimum0
Maximum32
Zeros110050
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size874.0 KiB
2022-04-04T01:11:40.755745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum32
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3139080244
Coefficient of variation (CV)11.8945833
Kurtosis1766.305574
Mean0.0263908383
Median Absolute Deviation (MAD)0
Skewness31.48082153
Sum2952
Variance0.09853824779
MonotonicityNot monotonic
2022-04-04T01:11:40.936086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0110050
98.4%
11384
 
1.2%
2215
 
0.2%
372
 
0.1%
441
 
< 0.1%
527
 
< 0.1%
618
 
< 0.1%
711
 
< 0.1%
910
 
< 0.1%
89
 
< 0.1%
Other values (8)20
 
< 0.1%
ValueCountFrequency (%)
0110050
98.4%
11384
 
1.2%
2215
 
0.2%
372
 
0.1%
441
 
< 0.1%
527
 
< 0.1%
618
 
< 0.1%
711
 
< 0.1%
89
 
< 0.1%
910
 
< 0.1%
ValueCountFrequency (%)
321
 
< 0.1%
211
 
< 0.1%
163
 
< 0.1%
151
 
< 0.1%
133
 
< 0.1%
124
 
< 0.1%
113
 
< 0.1%
104
 
< 0.1%
910
< 0.1%
89
< 0.1%

Average View Percentage
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct109659
Distinct (%)99.2%
Missing1347
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean0.3504564736
Minimum0
Maximum8.476339587
Zeros80
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size874.0 KiB
2022-04-04T01:11:41.140385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02200806075
Q10.1780102378
median0.3368243939
Q30.4762565232
95-th percentile0.7813672404
Maximum8.476339587
Range8.476339587
Interquartile range (IQR)0.2982462854

Descriptive statistics

Standard deviation0.232565626
Coefficient of variation (CV)0.663607733
Kurtosis20.62733331
Mean0.3504564736
Median Absolute Deviation (MAD)0.1491538044
Skewness1.537137424
Sum38728.9449
Variance0.05408677038
MonotonicityNot monotonic
2022-04-04T01:11:41.372129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186
 
0.1%
080
 
0.1%
0.983156862727
 
< 0.1%
0.99667204322
 
< 0.1%
0.997418539321
 
< 0.1%
0.996854330716
 
< 0.1%
0.99787515
 
< 0.1%
0.998932142915
 
< 0.1%
0.998323529414
 
< 0.1%
0.999551051113
 
< 0.1%
Other values (109649)110201
98.5%
(Missing)1347
 
1.2%
ValueCountFrequency (%)
080
0.1%
3.311258278 × 10-61
 
< 0.1%
4.426737494 × 10-61
 
< 0.1%
8.426966292 × 10-61
 
< 0.1%
1.805054152 × 10-51
 
< 0.1%
1.812688822 × 10-51
 
< 0.1%
1.988466892 × 10-51
 
< 0.1%
2.102102102 × 10-51
 
< 0.1%
2.213368747 × 10-51
 
< 0.1%
3.979591837 × 10-51
 
< 0.1%
ValueCountFrequency (%)
8.4763395871
< 0.1%
6.0811827961
< 0.1%
4.1268013541
< 0.1%
3.9342071561
< 0.1%
3.9017027361
< 0.1%
3.8859985261
< 0.1%
2.9438202251
< 0.1%
2.8371045451
< 0.1%
2.6715670441
< 0.1%
2.595593221
< 0.1%

Average Watch Time
Real number (ℝ≥0)

MISSING

Distinct106978
Distinct (%)96.8%
Missing1347
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean216.7694276
Minimum0
Maximum5322.3
Zeros80
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size874.0 KiB
2022-04-04T01:11:41.583431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.835
Q1117.3790804
median181.505
Q3268.1268875
95-th percentile520.7164244
Maximum5322.3
Range5322.3
Interquartile range (IQR)150.7478071

Descriptive statistics

Standard deviation190.7096512
Coefficient of variation (CV)0.8797811266
Kurtosis68.09049549
Mean216.7694276
Median Absolute Deviation (MAD)73.09761538
Skewness5.186095552
Sum23955189.44
Variance36370.17106
MonotonicityNot monotonic
2022-04-04T01:11:41.802821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
080
 
0.1%
139
 
< 0.1%
2.928
 
< 0.1%
50.14127
 
< 0.1%
0.925
 
< 0.1%
324
 
< 0.1%
423
 
< 0.1%
1.922
 
< 0.1%
222
 
< 0.1%
185.38122
 
< 0.1%
Other values (106968)110198
98.5%
(Missing)1347
 
1.2%
ValueCountFrequency (%)
080
0.1%
0.0011
 
< 0.1%
0.0031
 
< 0.1%
0.0141
 
< 0.1%
0.0171
 
< 0.1%
0.0181
 
< 0.1%
0.023
 
< 0.1%
0.0241
 
< 0.1%
0.0351
 
< 0.1%
0.0361
 
< 0.1%
ValueCountFrequency (%)
5322.31
< 0.1%
5028.0211
< 0.1%
50281
< 0.1%
5027.661
< 0.1%
4701.7851
< 0.1%
4517.8891
< 0.1%
4517.151
< 0.1%
4517.0971
< 0.1%
4517.0811
< 0.1%
4224.9581
< 0.1%

User Comments Added
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size874.0 KiB
0
111857 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0111857
100.0%

Length

2022-04-04T01:11:41.995020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T01:11:42.109777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0111857
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-04T01:11:33.974654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:19.928852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:21.816654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:23.498558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:25.214301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:26.889539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:28.716019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:30.415047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:32.126401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:34.156931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:20.124998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:22.000711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:23.688978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:25.396971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:27.269040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:28.900652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:30.598262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:32.307312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:34.351860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:20.317798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:22.191608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:23.880049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:25.586372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:27.451248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:29.092803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:30.792081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:32.494934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:34.546525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:20.517245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:22.385109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:24.081627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:25.780929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:27.638144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:29.287830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:30.997305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:32.874226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:34.734433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:20.710485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:22.571315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:24.272575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:25.966407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:27.818367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:29.475236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:31.197493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:33.058459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:34.913507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:20.890734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:22.752813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:24.457191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:26.148941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:27.991640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:29.657671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:31.377670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:33.236403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:35.103815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:21.084339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:22.944358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:24.650784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:26.339079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:28.177614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:29.848108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:31.568740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:33.424143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:35.292778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:21.271122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:23.135898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:24.840312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:26.526430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:28.358360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:30.040172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:31.757609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:33.617752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:35.474485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:21.451355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:23.314996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:25.024929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:26.707437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:28.531435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:30.225899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:31.938293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T01:11:33.795089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-04T01:11:42.169996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-04T01:11:42.591834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-04T01:11:42.830357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-04T01:11:43.063704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-04T01:11:35.782806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-04T01:11:36.263210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-04T01:11:36.765953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-04T01:11:36.898324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateVideo TitleExternal Video IDVideo LengthThumbnail linkViewsVideo Likes AddedVideo Dislikes AddedVideo Likes RemovedUser Subscriptions AddedUser Subscriptions RemovedAverage View PercentageAverage Watch TimeUser Comments Added
019 Jan 2022Kaggle Project From Scratch - Part 2 (Exploratory Data Analysis)KQ80oD_boBM2191https://i.ytimg.com/vi/KQ80oD_boBM/hqdefault.jpg13000000.069055151.3001540
119 Jan 2022Welcome To My Channel | Ken Jee | Data SciencesmeFkHwnM_k51https://i.ytimg.com/vi/smeFkHwnM_k/hqdefault.jpg2000100.47125524.0340000
219 Jan 2022How She Dominated the FAANG Data Science Interview (@Tina Huang ) - KNN EP. 11vfV4nm004VQ2686https://i.ytimg.com/vi/vfV4nm004VQ/hqdefault.jpg10000000.126049338.5675000
319 Jan 2022The 9 Books That Changed My Perspective in 20193TrAYmrmA8o980https://i.ytimg.com/vi/3TrAYmrmA8o/hqdefault.jpg1000000.01150811.2780000
419 Jan 2022Interview with the Director of AI Research @ NVIDIA (Anima Anandkumar) - KNN EP. 07Xgg7dIKys9E2904https://i.ytimg.com/vi/Xgg7dIKys9E/hqdefault.jpg1000000.00754121.9000000
519 Jan 2022Data Science, Machine Learning, and AI: What's the Difference?q8cEt8gj3zY266https://i.ytimg.com/vi/q8cEt8gj3zY/hqdefault.jpg7000000.584489155.4741430
619 Jan 2022The PODCAST you might have asked for?tnpV1etgcxs139https://i.ytimg.com/vi/tnpV1etgcxs/hqdefault.jpg1000000.0458136.3680000
719 Jan 2022#66DaysOfData Round 3 Live Event! (feat. @StatQuest with Josh Starmer)qUK5Vk4NvBw3735https://i.ytimg.com/vi/qUK5Vk4NvBw/hqdefault.jpg2000000.01709563.8500000
819 Jan 20225 Proven Strategies to Break into a Data Science JobUpaEjBOMNqs334https://i.ytimg.com/vi/UpaEjBOMNqs/hqdefault.jpg1000000.25434184.9500000
919 Jan 2022Reviewing Your Data Science Projects - Episode 7 (Incredible Portfolio Website)txR8_jGi0Ls904https://i.ytimg.com/vi/txR8_jGi0Ls/hqdefault.jpg4000000.362091327.3302500

Last rows

DateVideo TitleExternal Video IDVideo LengthThumbnail linkViewsVideo Likes AddedVideo Dislikes AddedVideo Likes RemovedUser Subscriptions AddedUser Subscriptions RemovedAverage View PercentageAverage Watch TimeUser Comments Added
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11184826 Nov 2017Predicting Crypto-Currency Price Using RNN lSTM & GRUqfRhKHV8-t4311https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg3000000.509827158.5563330
11184925 Nov 2017Predicting Crypto-Currency Price Using RNN lSTM & GRUqfRhKHV8-t4311https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg1000000.23384672.7260000
11185024 Nov 2017Predicting Crypto-Currency Price Using RNN lSTM & GRUqfRhKHV8-t4311https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg1000000.872392271.3140000
11185122 Nov 2017Predicting Crypto-Currency Price Using RNN lSTM & GRUqfRhKHV8-t4311https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg2000000.502150156.1685000
11185221 Nov 2017Predicting Crypto-Currency Price Using RNN lSTM & GRUqfRhKHV8-t4311https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg2000000.693108215.5565000
11185320 Nov 2017Predicting Crypto-Currency Price Using RNN lSTM & GRUqfRhKHV8-t4311https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg9000000.492501153.1676670
11185419 Nov 2017Predicting Crypto-Currency Price Using RNN lSTM & GRUqfRhKHV8-t4311https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg4000000.08726827.1402500
11185518 Nov 2017Predicting Crypto-Currency Price Using RNN lSTM & GRUqfRhKHV8-t4311https://i.ytimg.com/vi/qfRhKHV8-t4/hqdefault.jpg13000000.444176138.1387690
1118561 Nov 2017ProjectDemoCSC478_UFCFightData5p73cIRYCZg729https://i.ytimg.com/vi/5p73cIRYCZg/hqdefault.jpg1000000.0030182.2000000